Artificial Intelligence (AI) is profoundly changing the way the retail sector manages two key processes: sales forecasting and labor shift scheduling. Data analysis, machine learning, and technological integrations are replacing traditional estimations with evidence-based decisions, leading to visible impacts on efficiency, costs, and quality of service.

Sales Forecasting: Beyond the Averages

Predicting sales no longer relies solely on experience or historical data from previous years. Tools like Point of Sale (POS) systems, CRMs, and predictive analytics engines allow retailers to anticipate consumer behavior with greater accuracy.

These systems, fueled by historical data and external variables (weather, events, trends), generate projections that facilitate strategic decision-making: from inventory stocking to personnel requirements.

The main observed advantages include:

• Higher accuracy in operational planning.
• Dynamic adjustments based on real variations in demand.
• Reduction of stockouts (products).
• Reduction of overstaffing during shifts (human capital).

AI Applied to Shift Management

More accurate sales forecasting is only useful if it translates into an efficient organization of work teams. At this point, AI has also transformed shift planning, allowing personnel assignments to be adjusted based on the estimated demand.

Through algorithm-based solutions, it is possible to:

• Calculate how many people are needed, at what times, and for which functions.
• Minimize overtime and unbalanced workloads.
• Guarantee a better experience for both the customer and the employee.

Success Stories: How AI Drives Concrete Results in Retail

At Shift we combine data analysis, artificial intelligence, and strategic support to improve human capital planning in the retail sector. The implementation of AI-based prediction models allows, for example, to more accurately estimate daily demand in stores, identify critical hours, and adjust the necessary number of employees per hour and function.

Aníbal Rojas, Head of Modeling at Shift, comments:

“Our mission is to translate data into decisions during planning. For example, we worked with a supermarket chain in Mexico that needed to optimize the distribution of personnel among cash registers, restocking, and customer service. We used mathematical models to assign shifts by function and time, considering restrictions such as holidays, licenses, and peak demand. This allowed us to reduce overload during critical moments and ensure adequate coverage during high-traffic hours.”

In Chile, another case illustrates how AI can make a difference:

“During key campaigns like Cyber Monday and Christmas, a renowned department store chain faced enormous pressure to respond to the high influx of customers. We worked together to anticipate demand behavior, understanding how promotions, local events, and holidays affected each point of sale. With that information, we redesigned the shifts to be more flexible and dynamic, which not only reduced over-assignment during off-peak hours but also allowed us to strategically reinforce service during peaks. The result: more sales, shorter queues, and a smoother shopping experience during decisive times of the year.”

Finally, Aníbal mentions a recurring challenge in the sector:

“The manual management of compensation for shifts worked on holidays generated errors and resource loss in companies with thousands of employees. In one of these cases, we automated the calculation of compensation, incorporating specific rules for each operation. We validated the solution with the client and adjusted the model to adapt it to their reality, eliminating errors and simplifying the process.”

The overall results achieved include:

• Up to 15% savings in personnel costs, through personalized planning.
• Reduction of up to 65% in overtime, thanks to precise shift assignment.
• Improvements in labor productivity, with increases of up to 8%.
• Payback between 5 and 8 months after implementation.

These results are achieved through a process that begins with a detailed diagnosis of the operation, followed by a modeling of demand for each area and job function for each moment of the day, and culminates with technological integration and continuous monitoring by the Customer Success team.

As Sandra Torres, a specialist from the Customer Success team at Shift, comments:

“Our job is to ensure that technology is not just a tool, but a real solution for the day-to-day of each client. We are in constant contact to adjust models, resolve doubts, and anticipate needs. We know that every company is different, and that is why, beyond AI, human support is key to achieving sustainable results.”

Interoperable Technology: A Key Factor

A fundamental aspect for the success of these projects has been the software’s ability to integrate with existing platforms: ERP, HSM, POS, among others. This interoperability avoids duplication and allows available historical data to be leveraged to feed the AI models.

Conclusion

The use of artificial intelligence in sales forecasting and shift management not only improves operational efficiency but also allows daily decisions to be adapted to changing contexts. In a sector like retail, where margins and customer experience are increasingly at stake, these tools are consolidating as strategic allies to achieve a more agile, precise, and sustainable operation.

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